Overview

Brought to you by YData

Dataset statistics

Number of variables29
Number of observations15712
Missing cells106947
Missing cells (%)23.5%
Duplicate rows20
Duplicate rows (%)0.1%
Total size in memory22.4 MiB
Average record size in memory1.5 KiB

Variable types

Numeric8
DateTime5
Categorical6
Text8
Unsupported2

Alerts

Check-out has constant value "2024-08-24 11:00:00"Constant
Dataset has 20 (0.1%) duplicate rowsDuplicates
Adelanto ya pagado is highly overall correlated with ComisiĂ³n incluida and 2 other fieldsHigh correlation
Adultos is highly overall correlated with Numero_Huespedes and 2 other fieldsHigh correlation
Apartamento is highly overall correlated with Pago por adelantadoHigh correlation
ComisiĂ³n incluida is highly overall correlated with Adelanto ya pagado and 2 other fieldsHigh correlation
Estado is highly overall correlated with Pago por adelantadoHigh correlation
Niños is highly overall correlated with Pago por adelantadoHigh correlation
Numero_Huespedes is highly overall correlated with Adultos and 2 other fieldsHigh correlation
Numero_Reserva is highly overall correlated with Adelanto ya pagado and 2 other fieldsHigh correlation
NĂºmero de noches is highly overall correlated with ComisiĂ³n incluida and 2 other fieldsHigh correlation
Pagado is highly overall correlated with Adultos and 3 other fieldsHigh correlation
Pago por adelantado is highly overall correlated with Adelanto ya pagado and 9 other fieldsHigh correlation
Portal de reserva is highly overall correlated with Numero_Reserva and 2 other fieldsHigh correlation
PosiciĂ³n is highly overall correlated with Numero_ReservaHigh correlation
Precio is highly overall correlated with ComisiĂ³n incluida and 2 other fieldsHigh correlation
Adelanto ya pagado is highly imbalanced (99.6%)Imbalance
Estado is highly imbalanced (67.0%)Imbalance
Huésped has 1375 (8.8%) missing valuesMissing
Email has 4803 (30.6%) missing valuesMissing
Teléfono has 2568 (16.3%) missing valuesMissing
DirecciĂ³n has 15712 (100.0%) missing valuesMissing
Check-in has 4759 (30.3%) missing valuesMissing
Check-out has 4857 (30.9%) missing valuesMissing
Notas has 2272 (14.5%) missing valuesMissing
Precio has 1469 (9.3%) missing valuesMissing
Detalles de precios has 3063 (19.5%) missing valuesMissing
ComisiĂ³n incluida has 3075 (19.6%) missing valuesMissing
City tax has 15712 (100.0%) missing valuesMissing
Pago por adelantado has 15708 (> 99.9%) missing valuesMissing
Nota para colaboradores has 15703 (99.9%) missing valuesMissing
Numero_Reserva has 5285 (33.6%) missing valuesMissing
Mensaje_Huesped has 5293 (33.7%) missing valuesMissing
BOOKING_NOTE has 5293 (33.7%) missing valuesMissing
NĂºmero de noches is highly skewed (γ1 = 118.2859681)Skewed
DirecciĂ³n is an unsupported type, check if it needs cleaning or further analysisUnsupported
City tax is an unsupported type, check if it needs cleaning or further analysisUnsupported
Adultos has 2444 (15.6%) zerosZeros
Niños has 12790 (81.4%) zerosZeros
Numero_Huespedes has 2442 (15.5%) zerosZeros

Reproduction

Analysis started2024-08-24 21:42:39.367197
Analysis finished2024-08-24 21:45:01.522962
Duration2 minutes and 22.16 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

PosiciĂ³n
Real number (ℝ)

HIGH CORRELATION 

Distinct15692
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41865996
Minimum15390136
Maximum69854666
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size122.9 KiB
2024-08-24T23:45:01.603701image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum15390136
5-th percentile19849936
Q129516090
median41444873
Q353547937
95-th percentile66463759
Maximum69854666
Range54464530
Interquartile range (IQR)24031847

Descriptive statistics

Standard deviation14579774
Coefficient of variation (CV)0.3482486
Kurtosis-1.0654767
Mean41865996
Median Absolute Deviation (MAD)11998268
Skewness0.11885661
Sum6.5779852 Ă— 1011
Variance2.1256982 Ă— 1014
MonotonicityNot monotonic
2024-08-24T23:45:01.720320image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50757695 2
 
< 0.1%
38091838 2
 
< 0.1%
31968127 2
 
< 0.1%
49003082 2
 
< 0.1%
32169718 2
 
< 0.1%
49378100 2
 
< 0.1%
50677661 2
 
< 0.1%
50878493 2
 
< 0.1%
50895260 2
 
< 0.1%
29995319 2
 
< 0.1%
Other values (15682) 15692
99.9%
ValueCountFrequency (%)
15390136 1
< 0.1%
15463501 1
< 0.1%
15776323 1
< 0.1%
15918979 1
< 0.1%
16641582 1
< 0.1%
16811706 1
< 0.1%
16912662 1
< 0.1%
16982857 1
< 0.1%
16993282 1
< 0.1%
16993297 1
< 0.1%
ValueCountFrequency (%)
69854666 1
< 0.1%
69849111 1
< 0.1%
69847911 1
< 0.1%
69843366 1
< 0.1%
69840491 1
< 0.1%
69840476 1
< 0.1%
69838566 1
< 0.1%
69834011 1
< 0.1%
69825606 1
< 0.1%
69818721 1
< 0.1%
Distinct1024
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size122.9 KiB
Minimum2021-12-19 00:00:00
Maximum2024-12-11 00:00:00
2024-08-24T23:45:01.838347image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:45:01.965105image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Salida
Date

Distinct1023
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size122.9 KiB
Minimum2022-01-02 00:00:00
Maximum2028-07-31 00:00:00
2024-08-24T23:45:02.070342image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:45:02.190977image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Apartamento
Categorical

HIGH CORRELATION 

Distinct41
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
H BMA PRAGA
 
734
HD BRUNO
 
713
H BMA BERLIN
 
710
H - BUA 3P
 
697
H - BUA 4P
 
685
Other values (36)
12173 

Length

Max length21
Median length16
Mean length10.908923
Min length8

Characters and Unicode

Total characters171401
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHD OLIVIA
2nd rowHD FIDEL
3rd rowHD ALEJANDRA
4th rowH BMA LISBOA
5th rowHD OLIVIA

Common Values

ValueCountFrequency (%)
H BMA PRAGA 734
 
4.7%
HD BRUNO 713
 
4.5%
H BMA BERLIN 710
 
4.5%
H - BUA 3P 697
 
4.4%
H - BUA 4P 685
 
4.4%
HD ELENA 638
 
4.1%
H BMA AMSTERDAM 637
 
4.1%
H BMA MONACO 625
 
4.0%
H BMA OSLO 623
 
4.0%
HD CELESTE 600
 
3.8%
Other values (31) 9050
57.6%

Length

2024-08-24T23:45:02.306777image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
h 7270
17.9%
bma 5788
 
14.3%
hd 5373
 
13.2%
1482
 
3.7%
bua 1482
 
3.7%
hg0 1362
 
3.4%
hg1 975
 
2.4%
praga 734
 
1.8%
bruno 713
 
1.8%
berlin 710
 
1.7%
Other values (41) 14700
36.2%

Most occurring characters

ValueCountFrequency (%)
24877
14.5%
A 23487
13.7%
H 17721
 
10.3%
B 10659
 
6.2%
D 9603
 
5.6%
E 8425
 
4.9%
M 8323
 
4.9%
R 8219
 
4.8%
I 7818
 
4.6%
O 7197
 
4.2%
Other values (23) 45072
26.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 171401
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
24877
14.5%
A 23487
13.7%
H 17721
 
10.3%
B 10659
 
6.2%
D 9603
 
5.6%
E 8425
 
4.9%
M 8323
 
4.9%
R 8219
 
4.8%
I 7818
 
4.6%
O 7197
 
4.2%
Other values (23) 45072
26.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 171401
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
24877
14.5%
A 23487
13.7%
H 17721
 
10.3%
B 10659
 
6.2%
D 9603
 
5.6%
E 8425
 
4.9%
M 8323
 
4.9%
R 8219
 
4.8%
I 7818
 
4.6%
O 7197
 
4.2%
Other values (23) 45072
26.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 171401
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
24877
14.5%
A 23487
13.7%
H 17721
 
10.3%
B 10659
 
6.2%
D 9603
 
5.6%
E 8425
 
4.9%
M 8323
 
4.9%
R 8219
 
4.8%
I 7818
 
4.6%
O 7197
 
4.2%
Other values (23) 45072
26.3%

Huésped
Text

MISSING 

Distinct12201
Distinct (%)85.1%
Missing1375
Missing (%)8.8%
Memory size1.2 MiB
2024-08-24T23:45:02.606742image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length127
Median length63
Mean length16.398131
Min length1

Characters and Unicode

Total characters235100
Distinct characters241
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11300 ?
Unique (%)78.8%

Sample

1st rowLeticia Isla
2nd rowRodri Gamboa
3rd rowluis
4th rowNATHAN benony
5th rowDario
ValueCountFrequency (%)
de 338
 
1.0%
maria 307
 
0.9%
garcia 256
 
0.7%
jose 229
 
0.7%
rodriguez 180
 
0.5%
juan 172
 
0.5%
javier 159
 
0.5%
martinez 154
 
0.4%
ana 153
 
0.4%
amplio 153
 
0.4%
Other values (12026) 32730
94.0%
2024-08-24T23:45:03.053584image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 26695
 
11.4%
20561
 
8.7%
e 18914
 
8.0%
r 16109
 
6.9%
i 14381
 
6.1%
o 13819
 
5.9%
n 13444
 
5.7%
l 10297
 
4.4%
s 7222
 
3.1%
t 5901
 
2.5%
Other values (231) 87757
37.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 235100
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 26695
 
11.4%
20561
 
8.7%
e 18914
 
8.0%
r 16109
 
6.9%
i 14381
 
6.1%
o 13819
 
5.9%
n 13444
 
5.7%
l 10297
 
4.4%
s 7222
 
3.1%
t 5901
 
2.5%
Other values (231) 87757
37.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 235100
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 26695
 
11.4%
20561
 
8.7%
e 18914
 
8.0%
r 16109
 
6.9%
i 14381
 
6.1%
o 13819
 
5.9%
n 13444
 
5.7%
l 10297
 
4.4%
s 7222
 
3.1%
t 5901
 
2.5%
Other values (231) 87757
37.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 235100
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 26695
 
11.4%
20561
 
8.7%
e 18914
 
8.0%
r 16109
 
6.9%
i 14381
 
6.1%
o 13819
 
5.9%
n 13444
 
5.7%
l 10297
 
4.4%
s 7222
 
3.1%
t 5901
 
2.5%
Other values (231) 87757
37.3%

Portal de reserva
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
Booking.com
10439 
Airbnb
2223 
Reserva directa
1419 
Cerrar fechas (bloqueo)
1326 
PĂ¡gina web
 
305

Length

Max length23
Median length11
Mean length11.647149
Min length6

Characters and Unicode

Total characters183000
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCerrar fechas (bloqueo)
2nd rowCerrar fechas (bloqueo)
3rd rowCerrar fechas (bloqueo)
4th rowCerrar fechas (bloqueo)
5th rowCerrar fechas (bloqueo)

Common Values

ValueCountFrequency (%)
Booking.com 10439
66.4%
Airbnb 2223
 
14.1%
Reserva directa 1419
 
9.0%
Cerrar fechas (bloqueo) 1326
 
8.4%
PĂ¡gina web 305
 
1.9%

Length

2024-08-24T23:45:03.170364image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-24T23:45:03.270227image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
booking.com 10439
52.0%
airbnb 2223
 
11.1%
reserva 1419
 
7.1%
directa 1419
 
7.1%
cerrar 1326
 
6.6%
fechas 1326
 
6.6%
bloqueo 1326
 
6.6%
pĂ¡gina 305
 
1.5%
web 305
 
1.5%

Most occurring characters

ValueCountFrequency (%)
o 33969
18.6%
i 14386
 
7.9%
c 13184
 
7.2%
n 12967
 
7.1%
g 10744
 
5.9%
B 10439
 
5.7%
k 10439
 
5.7%
. 10439
 
5.7%
m 10439
 
5.7%
r 9039
 
4.9%
Other values (21) 46955
25.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 183000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 33969
18.6%
i 14386
 
7.9%
c 13184
 
7.2%
n 12967
 
7.1%
g 10744
 
5.9%
B 10439
 
5.7%
k 10439
 
5.7%
. 10439
 
5.7%
m 10439
 
5.7%
r 9039
 
4.9%
Other values (21) 46955
25.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 183000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 33969
18.6%
i 14386
 
7.9%
c 13184
 
7.2%
n 12967
 
7.1%
g 10744
 
5.9%
B 10439
 
5.7%
k 10439
 
5.7%
. 10439
 
5.7%
m 10439
 
5.7%
r 9039
 
4.9%
Other values (21) 46955
25.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 183000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 33969
18.6%
i 14386
 
7.9%
c 13184
 
7.2%
n 12967
 
7.1%
g 10744
 
5.9%
B 10439
 
5.7%
k 10439
 
5.7%
. 10439
 
5.7%
m 10439
 
5.7%
r 9039
 
4.9%
Other values (21) 46955
25.7%

Creado
Date

Distinct14803
Distinct (%)94.2%
Missing0
Missing (%)0.0%
Memory size122.9 KiB
Minimum2021-02-12 11:22:00
Maximum2024-12-08 23:16:00
2024-08-24T23:45:03.387392image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:45:03.503576image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Email
Text

MISSING 

Distinct10373
Distinct (%)95.1%
Missing4803
Missing (%)30.6%
Memory size1.1 MiB
2024-08-24T23:45:03.736982image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length40
Median length31
Mean length30.558988
Min length12

Characters and Unicode

Total characters333368
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10008 ?
Unique (%)91.7%

Sample

1st rowlisla.593122@guest.booking.com
2nd rowrgambo.386041@guest.booking.com
3rd rownbenon.480362@guest.booking.com
4th rowaaquit.901533@guest.booking.com
5th rowhlee.108965@guest.booking.com
ValueCountFrequency (%)
marycasadocarreto@gmail.com 13
 
0.1%
officialbryan09@aol.com 12
 
0.1%
institucional@castellersdevilafranca.cat 10
 
0.1%
carabalil@aol.com 10
 
0.1%
jvila.937171@guest.booking.com 8
 
0.1%
lsanz@paulmitchell.es 8
 
0.1%
yaling0404068@gmail.com 7
 
0.1%
ksaman.612410@guest.booking.com 6
 
0.1%
juanjomaruno@gmail.com 6
 
0.1%
jm.barquin@protiopower.com 6
 
0.1%
Other values (10362) 10823
99.2%
2024-08-24T23:45:04.070390image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 36144
 
10.8%
. 31987
 
9.6%
g 23229
 
7.0%
e 16231
 
4.9%
i 15104
 
4.5%
m 14642
 
4.4%
n 14067
 
4.2%
s 13868
 
4.2%
c 13815
 
4.1%
t 12751
 
3.8%
Other values (52) 141530
42.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 333368
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 36144
 
10.8%
. 31987
 
9.6%
g 23229
 
7.0%
e 16231
 
4.9%
i 15104
 
4.5%
m 14642
 
4.4%
n 14067
 
4.2%
s 13868
 
4.2%
c 13815
 
4.1%
t 12751
 
3.8%
Other values (52) 141530
42.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 333368
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 36144
 
10.8%
. 31987
 
9.6%
g 23229
 
7.0%
e 16231
 
4.9%
i 15104
 
4.5%
m 14642
 
4.4%
n 14067
 
4.2%
s 13868
 
4.2%
c 13815
 
4.1%
t 12751
 
3.8%
Other values (52) 141530
42.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 333368
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 36144
 
10.8%
. 31987
 
9.6%
g 23229
 
7.0%
e 16231
 
4.9%
i 15104
 
4.5%
m 14642
 
4.4%
n 14067
 
4.2%
s 13868
 
4.2%
c 13815
 
4.1%
t 12751
 
3.8%
Other values (52) 141530
42.5%

Teléfono
Text

MISSING 

Distinct11601
Distinct (%)88.3%
Missing2568
Missing (%)16.3%
Memory size1000.4 KiB
2024-08-24T23:45:04.439360image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length20
Median length16
Mean length14.67605
Min length9

Characters and Unicode

Total characters192902
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10713 ?
Unique (%)81.5%

Sample

1st row+34 656 73 69 43
2nd row+34 662 96 91 70
3rd row+33 6 17 12 54 98
4th row+34 670 90 62 94
5th row+82 421915930214
ValueCountFrequency (%)
34 7159
 
14.6%
33 1126
 
2.3%
6 928
 
1.9%
49 525
 
1.1%
31 510
 
1.0%
39 505
 
1.0%
44 499
 
1.0%
32 331
 
0.7%
70 315
 
0.6%
48 309
 
0.6%
Other values (6138) 36907
75.1%
2024-08-24T23:45:04.920171image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
35972
18.6%
3 23286
12.1%
6 21280
11.0%
4 20387
10.6%
7 12564
 
6.5%
1 12028
 
6.2%
9 11926
 
6.2%
5 11833
 
6.1%
2 11328
 
5.9%
0 11082
 
5.7%
Other values (2) 21216
11.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 192902
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
35972
18.6%
3 23286
12.1%
6 21280
11.0%
4 20387
10.6%
7 12564
 
6.5%
1 12028
 
6.2%
9 11926
 
6.2%
5 11833
 
6.1%
2 11328
 
5.9%
0 11082
 
5.7%
Other values (2) 21216
11.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 192902
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
35972
18.6%
3 23286
12.1%
6 21280
11.0%
4 20387
10.6%
7 12564
 
6.5%
1 12028
 
6.2%
9 11926
 
6.2%
5 11833
 
6.1%
2 11328
 
5.9%
0 11082
 
5.7%
Other values (2) 21216
11.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 192902
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
35972
18.6%
3 23286
12.1%
6 21280
11.0%
4 20387
10.6%
7 12564
 
6.5%
1 12028
 
6.2%
9 11926
 
6.2%
5 11833
 
6.1%
2 11328
 
5.9%
0 11082
 
5.7%
Other values (2) 21216
11.0%

DirecciĂ³n
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing15712
Missing (%)100.0%
Memory size122.9 KiB

Adultos
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2334521
Minimum0
Maximum8
Zeros2444
Zeros (%)15.6%
Negative0
Negative (%)0.0%
Memory size122.9 KiB
2024-08-24T23:45:05.020117image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum8
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5834759
Coefficient of variation (CV)0.70898135
Kurtosis0.89371826
Mean2.2334521
Median Absolute Deviation (MAD)1
Skewness0.78889935
Sum35092
Variance2.5073959
MonotonicityNot monotonic
2024-08-24T23:45:05.103651image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 6114
38.9%
0 2444
 
15.6%
3 2098
 
13.4%
1 1958
 
12.5%
4 1903
 
12.1%
5 528
 
3.4%
6 389
 
2.5%
7 198
 
1.3%
8 80
 
0.5%
ValueCountFrequency (%)
0 2444
 
15.6%
1 1958
 
12.5%
2 6114
38.9%
3 2098
 
13.4%
4 1903
 
12.1%
5 528
 
3.4%
6 389
 
2.5%
7 198
 
1.3%
8 80
 
0.5%
ValueCountFrequency (%)
8 80
 
0.5%
7 198
 
1.3%
6 389
 
2.5%
5 528
 
3.4%
4 1903
 
12.1%
3 2098
 
13.4%
2 6114
38.9%
1 1958
 
12.5%
0 2444
 
15.6%

Niños
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.32764766
Minimum0
Maximum7
Zeros12790
Zeros (%)81.4%
Negative0
Negative (%)0.0%
Memory size122.9 KiB
2024-08-24T23:45:05.203566image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.75253459
Coefficient of variation (CV)2.29678
Kurtosis5.4976049
Mean0.32764766
Median Absolute Deviation (MAD)0
Skewness2.3708238
Sum5148
Variance0.56630831
MonotonicityNot monotonic
2024-08-24T23:45:05.303919image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 12790
81.4%
2 1503
 
9.6%
1 1093
 
7.0%
3 272
 
1.7%
4 43
 
0.3%
5 6
 
< 0.1%
6 4
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 12790
81.4%
1 1093
 
7.0%
2 1503
 
9.6%
3 272
 
1.7%
4 43
 
0.3%
5 6
 
< 0.1%
6 4
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 4
 
< 0.1%
5 6
 
< 0.1%
4 43
 
0.3%
3 272
 
1.7%
2 1503
 
9.6%
1 1093
 
7.0%
0 12790
81.4%

Check-in
Date

MISSING 

Distinct16
Distinct (%)0.1%
Missing4759
Missing (%)30.3%
Memory size122.9 KiB
Minimum2024-08-24 00:00:00
Maximum2024-08-24 23:30:00
2024-08-24T23:45:05.386800image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:45:05.470092image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)

Check-out
Date

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing4857
Missing (%)30.9%
Memory size122.9 KiB
Minimum2024-08-24 11:00:00
Maximum2024-08-24 11:00:00
2024-08-24T23:45:05.570179image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:45:05.653861image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Notas
Text

MISSING 

Distinct12902
Distinct (%)96.0%
Missing2272
Missing (%)14.5%
Memory size7.5 MiB
2024-08-24T23:45:05.988707image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length1115
Median length921
Mean length246.22515
Min length3

Characters and Unicode

Total characters3309266
Distinct characters271
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12529 ?
Unique (%)93.2%

Sample

1st rowNĂºmero de reserva: 3509184945 Mensaje del huĂ©sped: ** THIS RESERVATION HAS BEEN PRE-PAID ** BOOKING NOTE : Payment charge is EUR 2.5839 MĂ¡s informaciĂ³n: booker_is_genius Pago por adelantado: 234.90EUR
2nd rowNĂºmero de reserva: 3677697588 Mensaje del huĂ©sped: ** THIS RESERVATION HAS BEEN PRE-PAID ** Approximate time of arrival: between 14:00 and 15:00 BOOKING NOTE : Payment charge is EUR 1.045 MĂ¡s informaciĂ³n: booker_is_genius Pago por adelantado: 95.00EUR
3rd rowNĂºmero de reserva: 2331981900 Mensaje del huĂ©sped: ** THIS RESERVATION HAS BEEN PRE-PAID ** Approximate time of arrival: between 17:00 and 18:00 BOOKING NOTE : Payment charge is EUR 2.695 MĂ¡s informaciĂ³n: booker_is_genius Pago por adelantado: 245.00EUR
4th rowTrjeta 28.12
5th rowNĂºmero de reserva: 3390950867 Mensaje del huĂ©sped: ** THIS RESERVATION HAS BEEN PRE-PAID ** BOOKING NOTE : Payment charge is EUR 2.211 MĂ¡s informaciĂ³n: booker_is_genius Pago por adelantado: 201.00EUR
ValueCountFrequency (%)
33710
 
7.4%
de 15271
 
3.3%
reserva 14363
 
3.1%
del 14235
 
3.1%
nĂºmero 12662
 
2.8%
huésped 12634
 
2.8%
por 11125
 
2.4%
mĂ¡s 10652
 
2.3%
has 10649
 
2.3%
is 10619
 
2.3%
Other values (26638) 310910
68.1%
2024-08-24T23:45:06.453464image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
395960
 
12.0%
e 239121
 
7.2%
a 181972
 
5.5%
o 132880
 
4.0%
r 127744
 
3.9%
n 122691
 
3.7%
s 120443
 
3.6%
i 114242
 
3.5%
d 91393
 
2.8%
E 86974
 
2.6%
Other values (261) 1695846
51.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3309266
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
395960
 
12.0%
e 239121
 
7.2%
a 181972
 
5.5%
o 132880
 
4.0%
r 127744
 
3.9%
n 122691
 
3.7%
s 120443
 
3.6%
i 114242
 
3.5%
d 91393
 
2.8%
E 86974
 
2.6%
Other values (261) 1695846
51.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3309266
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
395960
 
12.0%
e 239121
 
7.2%
a 181972
 
5.5%
o 132880
 
4.0%
r 127744
 
3.9%
n 122691
 
3.7%
s 120443
 
3.6%
i 114242
 
3.5%
d 91393
 
2.8%
E 86974
 
2.6%
Other values (261) 1695846
51.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3309266
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
395960
 
12.0%
e 239121
 
7.2%
a 181972
 
5.5%
o 132880
 
4.0%
r 127744
 
3.9%
n 122691
 
3.7%
s 120443
 
3.6%
i 114242
 
3.5%
d 91393
 
2.8%
E 86974
 
2.6%
Other values (261) 1695846
51.2%

Precio
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2520
Distinct (%)17.7%
Missing1469
Missing (%)9.3%
Infinite0
Infinite (%)0.0%
Mean230.485
Minimum1.15
Maximum7847.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size122.9 KiB
2024-08-24T23:45:06.553633image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1.15
5-th percentile45
Q181.95
median148
Q3272
95-th percentile674
Maximum7847.6
Range7846.45
Interquartile range (IQR)190.05

Descriptive statistics

Standard deviation292.40293
Coefficient of variation (CV)1.2686419
Kurtosis129.08129
Mean230.485
Median Absolute Deviation (MAD)77
Skewness7.743219
Sum3282797.8
Variance85499.472
MonotonicityNot monotonic
2024-08-24T23:45:06.685941image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65 280
 
1.8%
20 259
 
1.6%
71 220
 
1.4%
77 206
 
1.3%
59 163
 
1.0%
83 155
 
1.0%
60 141
 
0.9%
53.1 135
 
0.9%
40 132
 
0.8%
95 128
 
0.8%
Other values (2510) 12424
79.1%
(Missing) 1469
 
9.3%
ValueCountFrequency (%)
1.15 1
 
< 0.1%
2.25 1
 
< 0.1%
3 1
 
< 0.1%
8.91 1
 
< 0.1%
10 27
0.2%
11 1
 
< 0.1%
13 1
 
< 0.1%
13.5 1
 
< 0.1%
15 25
0.2%
17.25 1
 
< 0.1%
ValueCountFrequency (%)
7847.6 2
< 0.1%
7330 1
< 0.1%
5785 1
< 0.1%
5595.25 1
< 0.1%
4097 1
< 0.1%
3886 1
< 0.1%
3800 1
< 0.1%
3738.2 1
< 0.1%
3630 1
< 0.1%
3600 1
< 0.1%

Detalles de precios
Text

MISSING 

Distinct3735
Distinct (%)29.5%
Missing3063
Missing (%)19.5%
Memory size1.1 MiB
2024-08-24T23:45:07.039990image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length183
Median length123
Mean length26.488971
Min length11

Characters and Unicode

Total characters335059
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2083 ?
Unique (%)16.5%

Sample

1st rowTVA - EUR 21.35
2nd rowTVA - EUR 8.64
3rd rowTVA - EUR 22.27
4th rowIVA - EUR 18.27
5th rowTVA - EUR 20.27
ValueCountFrequency (%)
16463
22.2%
eur 16463
22.2%
iva 5329
 
7.2%
tva 5097
 
6.9%
cancellation 4448
 
6.0%
fee 2224
 
3.0%
host 2224
 
3.0%
payout 2224
 
3.0%
security 1585
 
2.1%
price 1585
 
2.1%
Other values (2963) 16475
22.2%
2024-08-24T23:45:07.504991image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
57654
 
17.2%
- 16463
 
4.9%
E 16463
 
4.9%
U 16463
 
4.9%
R 16463
 
4.9%
. 13870
 
4.1%
e 12066
 
3.6%
a 11128
 
3.3%
1 10765
 
3.2%
t 10489
 
3.1%
Other values (30) 153235
45.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 335059
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
57654
 
17.2%
- 16463
 
4.9%
E 16463
 
4.9%
U 16463
 
4.9%
R 16463
 
4.9%
. 13870
 
4.1%
e 12066
 
3.6%
a 11128
 
3.3%
1 10765
 
3.2%
t 10489
 
3.1%
Other values (30) 153235
45.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 335059
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
57654
 
17.2%
- 16463
 
4.9%
E 16463
 
4.9%
U 16463
 
4.9%
R 16463
 
4.9%
. 13870
 
4.1%
e 12066
 
3.6%
a 11128
 
3.3%
1 10765
 
3.2%
t 10489
 
3.1%
Other values (30) 153235
45.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 335059
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
57654
 
17.2%
- 16463
 
4.9%
E 16463
 
4.9%
U 16463
 
4.9%
R 16463
 
4.9%
. 13870
 
4.1%
e 12066
 
3.6%
a 11128
 
3.3%
1 10765
 
3.2%
t 10489
 
3.1%
Other values (30) 153235
45.7%

ComisiĂ³n incluida
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2229
Distinct (%)17.6%
Missing3075
Missing (%)19.6%
Infinite0
Infinite (%)0.0%
Mean35.101689
Minimum0.17
Maximum867.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size122.9 KiB
2024-08-24T23:45:07.603447image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0.17
5-th percentile8.1
Q113.35
median23.1
Q342.04
95-th percentile99.9
Maximum867.75
Range867.58
Interquartile range (IQR)28.69

Descriptive statistics

Standard deviation38.59222
Coefficient of variation (CV)1.0994406
Kurtosis60.283014
Mean35.101689
Median Absolute Deviation (MAD)11.55
Skewness5.3086587
Sum443580.04
Variance1489.3595
MonotonicityNot monotonic
2024-08-24T23:45:07.703404image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.75 264
 
1.7%
10.65 224
 
1.4%
11.55 197
 
1.3%
12.45 162
 
1.0%
8.85 160
 
1.0%
7.96 135
 
0.9%
21.3 126
 
0.8%
14.25 125
 
0.8%
8.78 122
 
0.8%
17.55 111
 
0.7%
Other values (2219) 11011
70.1%
(Missing) 3075
 
19.6%
ValueCountFrequency (%)
0.17 1
< 0.1%
0.34 1
< 0.1%
0.45 1
< 0.1%
1.34 1
< 0.1%
1.5 1
< 0.1%
1.65 1
< 0.1%
1.95 1
< 0.1%
2.02 1
< 0.1%
2.25 1
< 0.1%
2.59 1
< 0.1%
ValueCountFrequency (%)
867.75 1
< 0.1%
839.29 1
< 0.1%
614.55 1
< 0.1%
582.9 1
< 0.1%
560.73 1
< 0.1%
502.95 1
< 0.1%
492 1
< 0.1%
457.5 2
< 0.1%
445.45 1
< 0.1%
443.27 1
< 0.1%

City tax
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing15712
Missing (%)100.0%
Memory size122.9 KiB

Pagado
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
SĂ­
11501 
No
4211 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters31424
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
SĂ­ 11501
73.2%
No 4211
 
26.8%

Length

2024-08-24T23:45:07.818949image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-24T23:45:07.886708image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
sĂ­ 11501
73.2%
no 4211
 
26.8%

Most occurring characters

ValueCountFrequency (%)
S 11501
36.6%
Ă­ 11501
36.6%
N 4211
 
13.4%
o 4211
 
13.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31424
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 11501
36.6%
Ă­ 11501
36.6%
N 4211
 
13.4%
o 4211
 
13.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31424
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 11501
36.6%
Ă­ 11501
36.6%
N 4211
 
13.4%
o 4211
 
13.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31424
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 11501
36.6%
Ă­ 11501
36.6%
N 4211
 
13.4%
o 4211
 
13.4%

Pago por adelantado
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)75.0%
Missing15708
Missing (%)> 99.9%
Memory size859.4 KiB
40.0
800.0
140.0

Length

Max length5
Median length4.5
Mean length4.5
Min length4

Characters and Unicode

Total characters18
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)50.0%

Sample

1st row40.0
2nd row800.0
3rd row40.0
4th row140.0

Common Values

ValueCountFrequency (%)
40.0 2
 
< 0.1%
800.0 1
 
< 0.1%
140.0 1
 
< 0.1%
(Missing) 15708
> 99.9%

Length

2024-08-24T23:45:07.970324image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-24T23:45:08.055703image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
40.0 2
50.0%
800.0 1
25.0%
140.0 1
25.0%

Most occurring characters

ValueCountFrequency (%)
0 9
50.0%
. 4
22.2%
4 3
 
16.7%
8 1
 
5.6%
1 1
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 9
50.0%
. 4
22.2%
4 3
 
16.7%
8 1
 
5.6%
1 1
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 9
50.0%
. 4
22.2%
4 3
 
16.7%
8 1
 
5.6%
1 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 9
50.0%
. 4
22.2%
4 3
 
16.7%
8 1
 
5.6%
1 1
 
5.6%

Adelanto ya pagado
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size905.5 KiB
No
15707 
SĂ­
 
5

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters31424
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 15707
> 99.9%
SĂ­ 5
 
< 0.1%

Length

2024-08-24T23:45:08.141599image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-24T23:45:08.219995image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
no 15707
> 99.9%
sĂ­ 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 15707
50.0%
o 15707
50.0%
S 5
 
< 0.1%
Ă­ 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31424
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 15707
50.0%
o 15707
50.0%
S 5
 
< 0.1%
Ă­ 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31424
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 15707
50.0%
o 15707
50.0%
S 5
 
< 0.1%
Ă­ 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31424
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 15707
50.0%
o 15707
50.0%
S 5
 
< 0.1%
Ă­ 5
 
< 0.1%

NĂºmero de noches
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct37
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9168152
Minimum1
Maximum1491
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size122.9 KiB
2024-08-24T23:45:08.303378image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum1491
Range1490
Interquartile range (IQR)1

Descriptive statistics

Standard deviation12.117033
Coefficient of variation (CV)6.3214407
Kurtosis14520.764
Mean1.9168152
Median Absolute Deviation (MAD)0
Skewness118.28597
Sum30117
Variance146.8225
MonotonicityNot monotonic
2024-08-24T23:45:08.403320image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
1 9425
60.0%
2 3572
 
22.7%
3 1482
 
9.4%
4 663
 
4.2%
5 270
 
1.7%
7 80
 
0.5%
6 78
 
0.5%
8 26
 
0.2%
9 13
 
0.1%
10 13
 
0.1%
Other values (27) 90
 
0.6%
ValueCountFrequency (%)
1 9425
60.0%
2 3572
 
22.7%
3 1482
 
9.4%
4 663
 
4.2%
5 270
 
1.7%
6 78
 
0.5%
7 80
 
0.5%
8 26
 
0.2%
9 13
 
0.1%
10 13
 
0.1%
ValueCountFrequency (%)
1491 1
 
< 0.1%
112 1
 
< 0.1%
91 1
 
< 0.1%
81 1
 
< 0.1%
62 2
 
< 0.1%
42 1
 
< 0.1%
40 2
 
< 0.1%
33 1
 
< 0.1%
31 10
0.1%
30 6
< 0.1%

Estado
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1012.8 KiB
Reservado
13902 
Cancelado
1795 
Overbooking
 
15

Length

Max length11
Median length9
Mean length9.0019094
Min length9

Characters and Unicode

Total characters141438
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReservado
2nd rowReservado
3rd rowReservado
4th rowReservado
5th rowReservado

Common Values

ValueCountFrequency (%)
Reservado 13902
88.5%
Cancelado 1795
 
11.4%
Overbooking 15
 
0.1%

Length

2024-08-24T23:45:08.520011image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-24T23:45:08.620349image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
reservado 13902
88.5%
cancelado 1795
 
11.4%
overbooking 15
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 29614
20.9%
a 17492
12.4%
o 15727
11.1%
d 15697
11.1%
r 13917
9.8%
v 13917
9.8%
R 13902
9.8%
s 13902
9.8%
n 1810
 
1.3%
l 1795
 
1.3%
Other values (7) 3665
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 141438
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 29614
20.9%
a 17492
12.4%
o 15727
11.1%
d 15697
11.1%
r 13917
9.8%
v 13917
9.8%
R 13902
9.8%
s 13902
9.8%
n 1810
 
1.3%
l 1795
 
1.3%
Other values (7) 3665
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 141438
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 29614
20.9%
a 17492
12.4%
o 15727
11.1%
d 15697
11.1%
r 13917
9.8%
v 13917
9.8%
R 13902
9.8%
s 13902
9.8%
n 1810
 
1.3%
l 1795
 
1.3%
Other values (7) 3665
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 141438
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 29614
20.9%
a 17492
12.4%
o 15727
11.1%
d 15697
11.1%
r 13917
9.8%
v 13917
9.8%
R 13902
9.8%
s 13902
9.8%
n 1810
 
1.3%
l 1795
 
1.3%
Other values (7) 3665
 
2.6%
Distinct9
Distinct (%)100.0%
Missing15703
Missing (%)99.9%
Memory size491.7 KiB
2024-08-24T23:45:08.753265image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length51
Median length31
Mean length22.333333
Min length13

Characters and Unicode

Total characters201
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)100.0%

Sample

1st rowTarjeta el 10
2nd rowPagado con tarjeta 07-06
3rd rowReserva hostel navarra adentro
4th rowpagado por tarjeta
5th rowdeja a la salida los 45€ registrar su DNI en chekin
ValueCountFrequency (%)
tarjeta 7
18.9%
el 5
 
13.5%
pagado 2
 
5.4%
salida 1
 
2.7%
28-07 1
 
2.7%
27-09 1
 
2.7%
02-10 1
 
2.7%
chekin 1
 
2.7%
en 1
 
2.7%
dni 1
 
2.7%
Other values (16) 16
43.2%
2024-08-24T23:45:09.004085image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 29
14.4%
28
13.9%
e 20
 
10.0%
r 15
 
7.5%
t 13
 
6.5%
l 9
 
4.5%
j 8
 
4.0%
0 8
 
4.0%
o 7
 
3.5%
s 6
 
3.0%
Other values (28) 58
28.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 201
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 29
14.4%
28
13.9%
e 20
 
10.0%
r 15
 
7.5%
t 13
 
6.5%
l 9
 
4.5%
j 8
 
4.0%
0 8
 
4.0%
o 7
 
3.5%
s 6
 
3.0%
Other values (28) 58
28.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 201
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 29
14.4%
28
13.9%
e 20
 
10.0%
r 15
 
7.5%
t 13
 
6.5%
l 9
 
4.5%
j 8
 
4.0%
0 8
 
4.0%
o 7
 
3.5%
s 6
 
3.0%
Other values (28) 58
28.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 201
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 29
14.4%
28
13.9%
e 20
 
10.0%
r 15
 
7.5%
t 13
 
6.5%
l 9
 
4.5%
j 8
 
4.0%
0 8
 
4.0%
o 7
 
3.5%
s 6
 
3.0%
Other values (28) 58
28.9%

Numero_Reserva
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10051
Distinct (%)96.4%
Missing5285
Missing (%)33.6%
Infinite0
Infinite (%)0.0%
Mean3.6453499 Ă— 109
Minimum1.0059188 Ă— 109
Maximum5.0001592 Ă— 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size122.9 KiB
2024-08-24T23:45:09.104879image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1.0059188 Ă— 109
5-th percentile2.2570196 Ă— 109
Q12.9378111 Ă— 109
median3.8202388 Ă— 109
Q34.2696162 Ă— 109
95-th percentile4.8358709 Ă— 109
Maximum5.0001592 Ă— 109
Range3.9942404 Ă— 109
Interquartile range (IQR)1.3318051 Ă— 109

Descriptive statistics

Standard deviation8.2927551 Ă— 108
Coefficient of variation (CV)0.22748859
Kurtosis-0.86882289
Mean3.6453499 Ă— 109
Median Absolute Deviation (MAD)6.1502163 Ă— 108
Skewness-0.36088964
Sum3.8010064 Ă— 1013
Variance6.8769786 Ă— 1017
MonotonicityNot monotonic
2024-08-24T23:45:09.221295image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4283082648 8
 
0.1%
4208534781 6
 
< 0.1%
4242031213 5
 
< 0.1%
3234695754 5
 
< 0.1%
4717565131 5
 
< 0.1%
4122679904 4
 
< 0.1%
2944693149 4
 
< 0.1%
3779868996 4
 
< 0.1%
4102548481 4
 
< 0.1%
4273851934 4
 
< 0.1%
Other values (10041) 10378
66.1%
(Missing) 5285
33.6%
ValueCountFrequency (%)
1005918761 1
< 0.1%
1005940672 1
< 0.1%
1007108187 1
< 0.1%
1015466631 1
< 0.1%
1021315351 1
< 0.1%
1021317261 1
< 0.1%
1021389587 1
< 0.1%
1029968432 1
< 0.1%
1062900362 1
< 0.1%
1062942205 1
< 0.1%
ValueCountFrequency (%)
5000159195 1
< 0.1%
4999865749 1
< 0.1%
4999810028 1
< 0.1%
4999203753 1
< 0.1%
4999049478 1
< 0.1%
4999029286 1
< 0.1%
4998728654 1
< 0.1%
4998674805 1
< 0.1%
4998670026 1
< 0.1%
4998637580 1
< 0.1%

Mensaje_Huesped
Text

MISSING 

Distinct1184
Distinct (%)11.4%
Missing5293
Missing (%)33.7%
Memory size1.4 MiB
2024-08-24T23:45:09.573587image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length824
Median length0
Mean length32.616182
Min length0

Characters and Unicode

Total characters339828
Distinct characters108
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1107 ?
Unique (%)10.6%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
and 1223
 
3.0%
of 1221
 
3.0%
time 1198
 
2.9%
arrival 1193
 
2.9%
between 1192
 
2.9%
approximate 1188
 
2.9%
en 900
 
2.2%
smart 859
 
2.1%
flex 859
 
2.1%
de 780
 
1.9%
Other values (2787) 30689
74.3%
2024-08-24T23:45:10.035251image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
38456
 
11.3%
e 27544
 
8.1%
a 25766
 
7.6%
t 19390
 
5.7%
i 19317
 
5.7%
n 18796
 
5.5%
o 17847
 
5.3%
r 14641
 
4.3%
s 13779
 
4.1%
l 11445
 
3.4%
Other values (98) 132847
39.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 339828
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
38456
 
11.3%
e 27544
 
8.1%
a 25766
 
7.6%
t 19390
 
5.7%
i 19317
 
5.7%
n 18796
 
5.5%
o 17847
 
5.3%
r 14641
 
4.3%
s 13779
 
4.1%
l 11445
 
3.4%
Other values (98) 132847
39.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 339828
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
38456
 
11.3%
e 27544
 
8.1%
a 25766
 
7.6%
t 19390
 
5.7%
i 19317
 
5.7%
n 18796
 
5.5%
o 17847
 
5.3%
r 14641
 
4.3%
s 13779
 
4.1%
l 11445
 
3.4%
Other values (98) 132847
39.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 339828
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
38456
 
11.3%
e 27544
 
8.1%
a 25766
 
7.6%
t 19390
 
5.7%
i 19317
 
5.7%
n 18796
 
5.5%
o 17847
 
5.3%
r 14641
 
4.3%
s 13779
 
4.1%
l 11445
 
3.4%
Other values (98) 132847
39.1%

BOOKING_NOTE
Text

MISSING 

Distinct2254
Distinct (%)21.6%
Missing5293
Missing (%)33.7%
Memory size1.0 MiB
2024-08-24T23:45:10.387752image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length30
Median length27
Mean length27.455226
Min length25

Characters and Unicode

Total characters286056
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1051 ?
Unique (%)10.1%

Sample

1st rowPayment charge is EUR 2.5839
2nd rowPayment charge is EUR 1.045
3rd rowPayment charge is EUR 2.695
4th rowPayment charge is EUR 2.211
5th rowPayment charge is EUR 2.453
ValueCountFrequency (%)
payment 10419
20.0%
eur 10419
20.0%
charge 10419
20.0%
is 10419
20.0%
0.715 175
 
0.3%
0.781 161
 
0.3%
0.649 156
 
0.3%
0.5841 136
 
0.3%
0.6435 121
 
0.2%
0.847 118
 
0.2%
Other values (2248) 9552
18.3%
2024-08-24T23:45:10.848315image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
41676
 
14.6%
e 20838
 
7.3%
a 20838
 
7.3%
P 10419
 
3.6%
g 10419
 
3.6%
. 10419
 
3.6%
R 10419
 
3.6%
U 10419
 
3.6%
s 10419
 
3.6%
i 10419
 
3.6%
Other values (18) 129771
45.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 286056
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
41676
 
14.6%
e 20838
 
7.3%
a 20838
 
7.3%
P 10419
 
3.6%
g 10419
 
3.6%
. 10419
 
3.6%
R 10419
 
3.6%
U 10419
 
3.6%
s 10419
 
3.6%
i 10419
 
3.6%
Other values (18) 129771
45.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 286056
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
41676
 
14.6%
e 20838
 
7.3%
a 20838
 
7.3%
P 10419
 
3.6%
g 10419
 
3.6%
. 10419
 
3.6%
R 10419
 
3.6%
U 10419
 
3.6%
s 10419
 
3.6%
i 10419
 
3.6%
Other values (18) 129771
45.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 286056
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
41676
 
14.6%
e 20838
 
7.3%
a 20838
 
7.3%
P 10419
 
3.6%
g 10419
 
3.6%
. 10419
 
3.6%
R 10419
 
3.6%
U 10419
 
3.6%
s 10419
 
3.6%
i 10419
 
3.6%
Other values (18) 129771
45.4%

Numero_Huespedes
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5610998
Minimum0
Maximum8
Zeros2442
Zeros (%)15.5%
Negative0
Negative (%)0.0%
Memory size122.9 KiB
2024-08-24T23:45:10.936672image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile6
Maximum8
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7650008
Coefficient of variation (CV)0.68915737
Kurtosis-0.031291712
Mean2.5610998
Median Absolute Deviation (MAD)1
Skewness0.45372578
Sum40240
Variance3.1152278
MonotonicityNot monotonic
2024-08-24T23:45:11.019975image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 4130
26.3%
4 3246
20.7%
0 2442
15.5%
3 2420
15.4%
1 1777
11.3%
5 778
 
5.0%
6 484
 
3.1%
7 315
 
2.0%
8 120
 
0.8%
ValueCountFrequency (%)
0 2442
15.5%
1 1777
11.3%
2 4130
26.3%
3 2420
15.4%
4 3246
20.7%
5 778
 
5.0%
6 484
 
3.1%
7 315
 
2.0%
8 120
 
0.8%
ValueCountFrequency (%)
8 120
 
0.8%
7 315
 
2.0%
6 484
 
3.1%
5 778
 
5.0%
4 3246
20.7%
3 2420
15.4%
2 4130
26.3%
1 1777
11.3%
0 2442
15.5%

Interactions

2024-08-24T23:44:50.955108image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:42:42.012109image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:42:50.982541image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:43:00.441787image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:43:10.027769image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:43:19.491808image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:43:29.240461image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:43:38.825923image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:44:51.038367image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:42:42.093725image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:42:51.059317image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:43:00.517888image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:43:10.108181image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:43:19.573680image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:43:29.321464image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:43:46.210160image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:44:51.136139image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:42:42.179361image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:42:51.142953image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:43:00.592512image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:43:10.191745image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:43:19.660143image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:43:29.411475image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:43:53.776986image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:44:51.224337image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:42:42.259603image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:42:51.242520image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:43:00.675468image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:43:10.275090image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:43:19.741556image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:43:29.491574image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:44:01.226444image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:44:51.304978image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:42:42.343039image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:42:51.341426image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:43:00.775884image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:43:10.377987image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:43:19.824969image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:43:29.591686image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:44:09.158365image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:44:51.388322image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:42:42.424953image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:42:51.412351image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:43:00.842191image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:43:10.441698image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:43:19.914085image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:43:29.657679image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:44:17.484442image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:44:51.507133image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:42:42.525859image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:42:52.026372image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:43:00.958379image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:43:10.561316image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:43:19.994495image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:43:29.779958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:44:25.662605image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:45:00.587064image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:42:50.892731image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:43:00.342368image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:43:09.925350image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:43:19.391565image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:43:29.143847image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:43:38.719159image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-24T23:44:43.074841image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Correlations

2024-08-24T23:45:11.103208image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Adelanto ya pagadoAdultosApartamentoComisiĂ³n incluidaEstadoNiñosNumero_HuespedesNumero_ReservaNĂºmero de nochesPagadoPago por adelantadoPortal de reservaPosiciĂ³nPrecio
Adelanto ya pagado1.0000.0240.0591.0000.0000.0000.0241.0000.0000.0240.7070.0540.0340.000
Adultos0.0241.0000.3290.3370.1060.0610.892-0.0210.0150.6091.0000.4740.1130.415
Apartamento0.0590.3291.0000.0740.0710.1110.3750.0000.0780.2841.0000.3950.1960.083
ComisiĂ³n incluida1.0000.3370.0741.0000.0690.1750.416-0.0100.7380.0260.0000.0230.0501.000
Estado0.0000.1060.0710.0691.0000.0060.1040.1200.0190.1251.0000.1300.0310.062
Niños0.0000.0610.1110.1750.0061.0000.467-0.0080.0240.1821.0000.1220.0530.196
Numero_Huespedes0.0240.8920.3750.4160.1040.4671.000-0.0260.0240.6101.0000.4730.1180.475
Numero_Reserva1.000-0.0210.000-0.0100.120-0.008-0.0261.000-0.0410.1880.0001.0000.802-0.010
NĂºmero de noches0.0000.0150.0780.7380.0190.0240.024-0.0411.0000.0001.0000.021-0.0080.677
Pagado0.0240.6090.2840.0260.1250.1820.6100.1880.0001.0001.0000.7320.4820.036
Pago por adelantado0.7071.0001.0000.0001.0001.0001.0000.0001.0001.0001.0001.0000.0000.707
Portal de reserva0.0540.4740.3950.0230.1300.1220.4731.0000.0210.7321.0001.0000.1500.035
PosiciĂ³n0.0340.1130.1960.0500.0310.0530.1180.802-0.0080.4820.0000.1501.0000.066
Precio0.0000.4150.0831.0000.0620.1960.475-0.0100.6770.0360.7070.0350.0661.000

Missing values

2024-08-24T23:45:00.754199image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-24T23:45:01.087016image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-08-24T23:45:01.354487image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

PosiciĂ³nLlegadaSalidaApartamentoHuĂ©spedPortal de reservaCreadoEmailTelĂ©fonoDirecciĂ³nAdultosNiñosCheck-inCheck-outNotasPrecioDetalles de preciosComisiĂ³n incluidaCity taxPagadoPago por adelantadoAdelanto ya pagadoNĂºmero de nochesEstadoNota para colaboradoresNumero_ReservaMensaje_HuespedBOOKING_NOTENumero_Huespedes
0325181472022-12-312023-01-01HD OLIVIANaNCerrar fechas (bloqueo)2022-12-31 21:56:00NaNNaNNaN0.00.02024-08-24 16:00:002024-08-24 11:00:00NaNNaNNaNNaNNaNNoNaNNo1ReservadoNaNNaNNaNNaN0.0
1325181442022-12-312023-01-01HD FIDELNaNCerrar fechas (bloqueo)2022-12-31 21:56:00NaNNaNNaN0.00.02024-08-24 16:00:002024-08-24 11:00:00NaNNaNNaNNaNNaNNoNaNNo1ReservadoNaNNaNNaNNaN0.0
2325181202022-12-312023-01-01HD ALEJANDRANaNCerrar fechas (bloqueo)2022-12-31 21:56:00NaNNaNNaN0.00.02024-08-24 16:00:002024-08-24 11:00:00NaNNaNNaNNaNNaNNoNaNNo1ReservadoNaNNaNNaNNaN0.0
3325181172022-12-312023-01-01H BMA LISBOANaNCerrar fechas (bloqueo)2022-12-31 21:55:00NaNNaNNaN0.00.0NaTNaTNaNNaNNaNNaNNaNNoNaNNo1ReservadoNaNNaNNaNNaN0.0
4324895302022-12-302022-12-31HD OLIVIANaNCerrar fechas (bloqueo)2022-12-30 22:03:00NaNNaNNaN0.00.02024-08-24 16:00:002024-08-24 11:00:00NaNNaNNaNNaNNaNNoNaNNo1ReservadoNaNNaNNaNNaN0.0
5324895212022-12-302022-12-31HD HUGHETNaNCerrar fechas (bloqueo)2022-12-30 22:02:00NaNNaNNaN0.00.02024-08-24 16:00:002024-08-24 11:00:00NaNNaNNaNNaNNaNNoNaNNo1ReservadoNaNNaNNaNNaN0.0
6324895182022-12-302022-12-31HD GLORIANaNCerrar fechas (bloqueo)2022-12-30 22:02:00NaNNaNNaN0.00.02024-08-24 16:00:002024-08-24 11:00:00NaNNaNNaNNaNNaNNoNaNNo1ReservadoNaNNaNNaNNaN0.0
7324895032022-12-302022-12-31HD FIDELNaNCerrar fechas (bloqueo)2022-12-30 22:02:00NaNNaNNaN0.00.02024-08-24 16:00:002024-08-24 11:00:00NaNNaNNaNNaNNaNNoNaNNo1ReservadoNaNNaNNaNNaN0.0
8324894732022-12-302022-12-31HD DARIONaNCerrar fechas (bloqueo)2022-12-30 22:01:00NaNNaNNaN0.00.02024-08-24 16:00:002024-08-24 11:00:00NaNNaNNaNNaNNaNNoNaNNo1ReservadoNaNNaNNaNNaN0.0
9324894492022-12-302022-12-31HD CELESTENaNCerrar fechas (bloqueo)2022-12-30 22:00:00NaNNaNNaN0.00.02024-08-24 16:00:002024-08-24 11:00:00NaNNaNNaNNaNNaNNoNaNNo1ReservadoNaNNaNNaNNaN0.0
PosiciĂ³nLlegadaSalidaApartamentoHuĂ©spedPortal de reservaCreadoEmailTelĂ©fonoDirecciĂ³nAdultosNiñosCheck-inCheck-outNotasPrecioDetalles de preciosComisiĂ³n incluidaCity taxPagadoPago por adelantadoAdelanto ya pagadoNĂºmero de nochesEstadoNota para colaboradoresNumero_ReservaMensaje_HuespedBOOKING_NOTENumero_Huespedes
15702490030822023-12-262024-05-01HD BRUNOCristina MaldonadoBooking.com2023-12-11 23:56:00cmaldo.123819@guest.booking.com+34 648 11 83 53NaN2.01.02024-08-24 16:00:002024-08-24 11:00:00NĂºmero de reserva: 4002704862\nMensaje del huĂ©sped: ** THIS RESERVATION HAS BEEN PRE-PAID ** BOOKING NOTE : Payment charge is EUR 13.98782\n\nDirecciĂ³n: Gh\nES\nMĂ¡s informaciĂ³n: booker_is_genius\nPago por adelantado: 1271.62EUR1271.62IVA - EUR 115.6190.74NaNSĂ­NaNNo10ReservadoNaN4002704862Payment charge is EUR 13.987823.0
15703488002132024-03-022024-05-02H BMA AMSTERDAMNoemi EspinosaBooking.com2023-08-11 16:16:00nespin.215286@guest.booking.com+44 7426761457NaN6.02.02024-08-24 16:00:002024-08-24 11:00:00NĂºmero de reserva: 4202562635\nMensaje del huĂ©sped: ** THIS RESERVATION HAS BEEN PRE-PAID ** BOOKING NOTE : Payment charge is EUR 6.0335\n\nMĂ¡s informaciĂ³n: booker_is_genius\nPago por adelantado: 548.50EUR548.50TVA - EUR 49.8682.28NaNSĂ­NaNNo2CanceladoNaN4202562635Payment charge is EUR 6.03358.0
15704486817942024-01-262024-01-28H BMA BERLINAlicia Solis PicattoBooking.com2023-06-11 12:50:00apicat.578133@guest.booking.com+34 684 62 58 24NaN6.00.02024-08-24 16:00:002024-08-24 11:00:00NĂºmero de reserva: 4128424493\nMensaje del huĂ©sped: ** THIS RESERVATION HAS BEEN PRE-PAID ** BOOKING NOTE : Payment charge is EUR 5.104\n\nDirecciĂ³n: Arturo Alvarez Buylla n4 6B\nOviedo\nES\nMĂ¡s informaciĂ³n: booker_is_genius\nPago por adelantado: 464.00EUR464.00TVA - EUR 42.1869.60NaNSĂ­NaNNo2ReservadoNaN4128424493Payment charge is EUR 5.1046.0
15705482180902024-02-252024-02-26H BMA MONACOJosĂ© luis ruiz martinezBooking.com2023-10-27 14:42:00ndqjfx.980350@guest.booking.com+34 627 23 87 92NaN2.02.0NaTNaTNĂºmero de reserva: 4254973846\nMensaje del huĂ©sped: ** THIS RESERVATION HAS BEEN PRE-PAID ** BOOKING NOTE : Payment charge is EUR 0.891 Approximate time of arrival: between 11:00 and 12:00\n\nMĂ¡s informaciĂ³n: booker_is_genius\nPago por adelantado: 81.00EUR81.00TVA - EUR 7.3612.15NaNSĂ­NaNNo1ReservadoNaN4254973846Approximate time of arrival: between 11:00 and 12:00Payment charge is EUR 0.8914.0
15706481790302024-01-222024-01-24HG1 ARTXANDAJorge Rubio AlcarazAirbnb2023-10-26 18:05:00NaN34666007165NaN7.00.02024-08-24 16:00:002024-08-24 11:00:00NĂºmero de reserva: HME2B4WMFN\nIdioma del huĂ©sped: es327.80Cancellation Host Fee - EUR 44.7\nCancellation Payout - EUR 283.144.70NaNNoNaNNo2CanceladoNaNNaNNaNNaN7.0
15707480203652024-06-172024-06-21H - BUA 3PNavarra AdentroReserva directa2023-10-23 12:17:00NaN659636268NaN1.00.02024-08-24 16:00:002024-08-24 11:00:00Viene con un grupo del hostel.520.00NaNNaNNaNNoNaNNo4ReservadoNaNNaNNaNNaN1.0
15708466706292024-11-022024-12-02H BMA AMSTERDAMCrespi CatalinaBooking.com2023-09-26 14:51:00ccatal.288618@guest.booking.com+34 617 49 18 56NaN7.00.02024-08-24 16:00:002024-08-24 11:00:00NĂºmero de reserva: 4232587055\nMensaje del huĂ©sped: ** THIS RESERVATION HAS BEEN PRE-PAID ** BOOKING NOTE : Payment charge is EUR 1.75175\n\nMĂ¡s informaciĂ³n: booker_is_genius\nPago por adelantado: 159.25EUR159.25TVA - EUR 14.4823.89NaNSĂ­NaNNo1CanceladoNaN4232587055Payment charge is EUR 1.751757.0
15709466423212024-12-012024-01-14H BMA BERLINMikel Zugasti RezolaBooking.com2023-09-25 23:26:00mrezol.585471@guest.booking.com+34 666 22 54 76NaN6.00.02024-08-24 16:00:002024-08-24 11:00:00NĂºmero de reserva: 4237415932\nMensaje del huĂ©sped: ** THIS RESERVATION HAS BEEN PRE-PAID ** Approximate time of arrival: between 17:00 and 18:00 BOOKING NOTE : Payment charge is EUR 13.1274\n\nMĂ¡s informaciĂ³n: booker_is_genius\nPago por adelantado: 1193.40EUR521.60TVA - EUR 47.4278.24NaNSĂ­NaNNo2CanceladoNaN4237415932Payment charge is EUR 13.12746.0
15710466423182024-12-012024-01-14H BMA AMSTERDAMMikel Zugasti RezolaBooking.com2023-09-25 23:26:00mrezol.585471@guest.booking.com+34 666 22 54 76NaN5.00.02024-08-24 16:00:002024-08-24 11:00:00NĂºmero de reserva: 4237415932\nMensaje del huĂ©sped: ** THIS RESERVATION HAS BEEN PRE-PAID ** Approximate time of arrival: between 17:00 and 18:00 BOOKING NOTE : Payment charge is EUR 13.1274\n\nMĂ¡s informaciĂ³n: booker_is_genius\nPago por adelantado: 1193.40EUR671.80TVA - EUR 61.07100.77NaNSĂ­NaNNo2CanceladoNaN4237415932Payment charge is EUR 13.12745.0
15711450055522024-08-012024-10-01HG0 MUGARRARoxana Palala MĂ©ndezAirbnb2023-08-26 11:09:00NaN50255354808NaN7.00.02024-08-24 16:00:002024-08-24 11:00:00NĂºmero de reserva: HM9ENYC99S\nIdioma del huĂ©sped: en261.80Cancellation Host Fee - EUR 35.7\nCancellation Payout - EUR 226.135.70NaNSĂ­NaNNo2ReservadoNaNNaNNaNNaN7.0

Duplicate rows

Most frequently occurring

PosiciĂ³nLlegadaSalidaApartamentoHuĂ©spedPortal de reservaCreadoEmailTelĂ©fonoAdultosNiñosCheck-inCheck-outNotasPrecioDetalles de preciosComisiĂ³n incluidaPagadoPago por adelantadoAdelanto ya pagadoNĂºmero de nochesEstadoNota para colaboradoresNumero_ReservaMensaje_HuespedBOOKING_NOTENumero_Huespedes# duplicates
0299953192022-12-292023-05-01H BMA OSLOJohannes SchmidAirbnb2022-10-22 16:02:00NaN4917035384862.00.0NaTNaTNĂºmero de reserva: HMDEHF2TH3\nIdioma del huĂ©sped: de1021.90Cancellation Host Fee - EUR 153.29\nCancellation Payout - EUR 868.61\nSecurity Price - EUR 1500153.29SĂ­NaNNo7ReservadoNaNNaNNaNNaN2.02
1319681272022-12-282023-07-01H BMA DUBLINNaNCerrar fechas (bloqueo)2022-12-15 12:46:00NaNNaN0.00.0NaTNaTNaNNaNNaNNaNNoNaNNo10CanceladoNaNNaNNaNNaN0.02
2321697182022-12-302023-05-01H - BUA 4PNaNCerrar fechas (bloqueo)2022-12-21 12:07:00NaNNaN0.00.02024-08-24 16:00:002024-08-24 11:00:00NaNNaNNaNNaNNoNaNNo6ReservadoNaNNaNNaNNaN0.02
3324690282022-12-312023-04-01H BMA GARAJEluisReserva directa2022-12-30 12:50:00NaNNaN0.00.0NaTNaTNaNNaNNaNNaNNoNaNNo4ReservadoNaNNaNNaNNaN0.02
4337963252023-02-242023-02-26H BMA MONACOSara Rodriguez CarvalhoBooking.com2023-01-25 00:44:00scarva.107440@guest.booking.com+351 964 835 7134.00.0NaTNaTNĂºmero de reserva: 3930128015\nMensaje del huĂ©sped: ** THIS RESERVATION HAS BEEN PRE-PAID ** BOOKING NOTE : Payment charge is EUR 6.523 Approximate time of arrival: between 00:00 and 01:00 the next day\n\nMĂ¡s informaciĂ³n: booker_is_genius\nPago por adelantado: 593.00EUR265.00TVA - EUR 24.0939.75SĂ­NaNNo2ReservadoNaN3930128015Approximate time of arrival: between 00:00 and 01:00 the next dayPayment charge is EUR 6.5234.02
5380918382023-05-202023-05-21HD ELENAMarina Losa PicornellBooking.com2023-04-16 22:19:00jgarau.803545@guest.booking.com+34 675 15 42 856.00.02024-08-24 16:00:002024-08-24 11:00:00NĂºmero de reserva: 2795551840\nMensaje del huĂ©sped: ** THIS RESERVATION HAS BEEN PRE-PAID ** BOOKING NOTE : Payment charge is EUR 4.99895\n\nMĂ¡s informaciĂ³n: booker_is_genius\nPago por adelantado: 454.45EUR257.35IVA - EUR 23.438.60SĂ­NaNNo1CanceladoNaN2795551840Payment charge is EUR 4.998956.02
6490030822023-12-262024-05-01HD BRUNOCristina MaldonadoBooking.com2023-12-11 23:56:00cmaldo.123819@guest.booking.com+34 648 11 83 532.01.02024-08-24 16:00:002024-08-24 11:00:00NĂºmero de reserva: 4002704862\nMensaje del huĂ©sped: ** THIS RESERVATION HAS BEEN PRE-PAID ** BOOKING NOTE : Payment charge is EUR 13.98782\n\nDirecciĂ³n: Gh\nES\nMĂ¡s informaciĂ³n: booker_is_genius\nPago por adelantado: 1271.62EUR1271.62IVA - EUR 115.6190.74SĂ­NaNNo10ReservadoNaN4002704862Payment charge is EUR 13.987823.02
7490828672023-12-262024-05-01HD-GARAJE 5brunoReserva directa2023-11-14 14:49:00NaNNaN0.00.0NaTNaTtarjeta el 14-11200.00NaNNaNNoNaNNo10ReservadoNaNNaNNaNNaN0.02
8493781002023-12-312024-02-01HD ELENALlorian Alvarez MartinezAirbnb2023-11-20 20:28:00NaN346442637656.00.02024-08-24 16:00:002024-08-24 11:00:00NĂºmero de reserva: HMAYKBSY9W\nIdioma del huĂ©sped: es571.00Cancellation Host Fee - EUR 85.65\nCancellation Payout - EUR 485.35\nSecurity Price - EUR 150085.65SĂ­NaNNo2ReservadoNaNNaNNaNNaN6.02
9497879992023-05-122024-05-02HD ALEJANDRAShirly a confirmarReserva directa2023-11-29 16:26:00NaNNaN0.00.02024-08-24 16:00:002024-08-24 11:00:00NaN7847.60NaNNaNNoNaNNo62CanceladoNaNNaNNaNNaN0.02